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dc.contributor.authorIglesias Morís, Daniel
dc.contributor.authorMoura, Joaquim de
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2024-05-02T11:59:16Z
dc.date.available2024-05-02T11:59:16Z
dc.date.issued2023
dc.identifier.citationD. I. Morís, J. de Moura, J. Novo, and M. Ortega, "Deep feature analysis in a transfer learning approach for the automatic COVID-19 screening using chest X-ray images", Procedia Computer Science, Vol. 225, pp. 228-237, doi: 10.1016/j.procs.2023.10.007es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36392
dc.description.abstract[Abstract]: COVID-19 is a challenging disease that was declared as global pandemic in March 2020. As the main impact of this disease is located in the pulmonary regions, chest X-ray devices are very useful to understand the severity of the disease on each patient. In order to reduce the risk of cross-contamination, the radiologists are recommended to use portable devices instead of fixed machinery, as these devices are easier to decontaminate. Moreover, the development of reliable and robust methodologies of computer-aided diagnosis systems is very relevant to reduce the workload that expert clinicians are experiencing in the current moment. In this work, we propose a comprehensive analysis of the deep features extracted from portable chest X-ray captures to perform a COVID-19 screening. We also study the optimal characterization of the problem with a lower dimensionality, contrasting the results of the feature selection methods that were chosen. Results demonstrated that the proposed approach is robust and reliable, obtaining a 90.43% of accuracy for the test set, using only 46.85% of the deep features in the context of poor quality and low detail X-ray images obtained from portable devices.es_ES
dc.description.sponsorshipThis work was supported by Ministerio de Ciencia e Innovación, Government of Spain through the research project with [grant numbers RTI2018-095894-B-I00, PID2019-108435RB-I00, TED2021-131201B-I00, and PDC2022-133132-I00]; Consellería de Educación, Universidade, e Formación Profesional, Xunta de Galicia, Grupos de Referencia Competitiva, [grant number ED431C 2020/24], predoctoral grant [grant number ED481A 2021/196]; CITIC, Centro de Investigación de Galicia [grant number ED431G 2019/01], receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secre-taría Xeral de Universidades (20%).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/24es_ES
dc.description.sponsorshipXunta de Galicia; ED481A 2021/196es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherElsevier B.V.es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICAes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLEes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/TED2021-131201B-I00/ES/DIAGNÓSTICO DIGITAL: TRANSFORMACIÓN DE LA DETECCIÓN DE ENFERMEDADES NEUROVASCULARES Y DEL TRATAMIENTO DE LOS PACIENTESes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/PDC2022-133132-I00/ES/MEJORAS EN EL DIAGNÓSTICO E INVESTIGACIÓN CLÍNICO MEDIANTE TECNOLOGÍAS INTELIGENTES APLICADAS LA IMAGEN OFTALMOLÓGICAes_ES
dc.relation.urihttps://doi.org/10.1016/j.procs.2023.10.007es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectcomputer-aided diagnosises_ES
dc.subjectCOVID-19es_ES
dc.subjectportable chest X-rayes_ES
dc.subjectdeep learninges_ES
dc.subjectdeep featureses_ES
dc.titleDeep feature analysis in a transfer learning approach for the automatic COVID-19 screening using chest X-ray imageses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProcedia Computer Sciencees_ES
UDC.volume225es_ES
UDC.startPage228es_ES
UDC.endPage237es_ES
dc.identifier.doi10.1016/j.procs.2023.10.007
UDC.conferenceTitleInternational Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)(27º. 2023. Athens, Greece)es_ES


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